Add new SentenceTransformer model with an onnx backend

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+onnx/model.onnx_data filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+    "word_embedding_dimension": 1024,
+    "pooling_mode_cls_token": false,
+    "pooling_mode_mean_tokens": true,
+    "pooling_mode_max_tokens": false,
+    "pooling_mode_mean_sqrt_len_tokens": false,
+    "pooling_mode_weightedmean_tokens": false,
+    "pooling_mode_lasttoken": false,
+    "include_prompt": true
+}

README.md

@@ -0,0 +1,810 @@
+---
+tags:
+- mteb
+model-index:
+- name: Solon-embeddings-large-0.1
+  results:
+  - task:
+      type: sentence-similarity
+      name: Passage Retrieval
+    dataset:
+      type: unicamp-dl/mmarco
+      name: mMARCO-fr
+      config: french
+      split: validation
+    metrics:
+      - type: recall_at_500
+        name: Recall@500
+        value: 92.7
+      - type: recall_at_100
+        name: Recall@100
+        value: 82.7
+      - type: recall_at_10
+        name: Recall@10
+        value: 55.5
+      - type: map_at_10
+        name: MAP@10
+        value: 29.4
+      - type: ndcg_at_10
+        name: nDCG@10
+        value: 35.8
+      - type: mrr_at_10
+        name: MRR@10
+        value: 29.9
+  - task:
+      type: Clustering
+    dataset:
+      type: lyon-nlp/alloprof
+      name: MTEB AlloProfClusteringP2P
+      config: default
+      split: test
+      revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
+    metrics:
+    - type: v_measure
+      value: 64.16942168287153
+  - task:
+      type: Clustering
+    dataset:
+      type: lyon-nlp/alloprof
+      name: MTEB AlloProfClusteringS2S
+      config: default
+      split: test
+      revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
+    metrics:
+    - type: v_measure
+      value: 38.17076313383054
+  - task:
+      type: Reranking
+    dataset:
+      type: lyon-nlp/mteb-fr-reranking-alloprof-s2p
+      name: MTEB AlloprofReranking
+      config: default
+      split: test
+      revision: 666fdacebe0291776e86f29345663dfaf80a0db9
+    metrics:
+    - type: map
+      value: 64.8770878097632
+    - type: mrr
+      value: 66.39132423169396
+  - task:
+      type: Retrieval
+    dataset:
+      type: lyon-nlp/alloprof
+      name: MTEB AlloprofRetrieval
+      config: default
+      split: test
+      revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
+    metrics:
+    - type: map_at_1
+      value: 29.62
+    - type: map_at_10
+      value: 40.963
+    - type: map_at_100
+      value: 41.894
+    - type: map_at_1000
+      value: 41.939
+    - type: map_at_3
+      value: 37.708999999999996
+    - type: map_at_5
+      value: 39.696999999999996
+    - type: mrr_at_1
+      value: 29.62
+    - type: mrr_at_10
+      value: 40.963
+    - type: mrr_at_100
+      value: 41.894
+    - type: mrr_at_1000
+      value: 41.939
+    - type: mrr_at_3
+      value: 37.708999999999996
+    - type: mrr_at_5
+      value: 39.696999999999996
+    - type: ndcg_at_1
+      value: 29.62
+    - type: ndcg_at_10
+      value: 46.942
+    - type: ndcg_at_100
+      value: 51.629999999999995
+    - type: ndcg_at_1000
+      value: 52.927
+    - type: ndcg_at_3
+      value: 40.333999999999996
+    - type: ndcg_at_5
+      value: 43.922
+    - type: precision_at_1
+      value: 29.62
+    - type: precision_at_10
+      value: 6.589
+    - type: precision_at_100
+      value: 0.882
+    - type: precision_at_1000
+      value: 0.099
+    - type: precision_at_3
+      value: 15.976
+    - type: precision_at_5
+      value: 11.33
+    - type: recall_at_1
+      value: 29.62
+    - type: recall_at_10
+      value: 65.889
+    - type: recall_at_100
+      value: 88.212
+    - type: recall_at_1000
+      value: 98.575
+    - type: recall_at_3
+      value: 47.927
+    - type: recall_at_5
+      value: 56.64900000000001
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (fr)
+      config: fr
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 42.077999999999996
+    - type: f1
+      value: 40.64511241732637
+  - task:
+      type: Retrieval
+    dataset:
+      type: maastrichtlawtech/bsard
+      name: MTEB BSARDRetrieval
+      config: default
+      split: test
+      revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59
+    metrics:
+    - type: map_at_1
+      value: 0.901
+    - type: map_at_10
+      value: 1.524
+    - type: map_at_100
+      value: 1.833
+    - type: map_at_1000
+      value: 1.916
+    - type: map_at_3
+      value: 1.276
+    - type: map_at_5
+      value: 1.276
+    - type: mrr_at_1
+      value: 0.901
+    - type: mrr_at_10
+      value: 1.524
+    - type: mrr_at_100
+      value: 1.833
+    - type: mrr_at_1000
+      value: 1.916
+    - type: mrr_at_3
+      value: 1.276
+    - type: mrr_at_5
+      value: 1.276
+    - type: ndcg_at_1
+      value: 0.901
+    - type: ndcg_at_10
+      value: 2.085
+    - type: ndcg_at_100
+      value: 3.805
+    - type: ndcg_at_1000
+      value: 6.704000000000001
+    - type: ndcg_at_3
+      value: 1.41
+    - type: ndcg_at_5
+      value: 1.41
+    - type: precision_at_1
+      value: 0.901
+    - type: precision_at_10
+      value: 0.40499999999999997
+    - type: precision_at_100
+      value: 0.126
+    - type: precision_at_1000
+      value: 0.037
+    - type: precision_at_3
+      value: 0.601
+    - type: precision_at_5
+      value: 0.36
+    - type: recall_at_1
+      value: 0.901
+    - type: recall_at_10
+      value: 4.054
+    - type: recall_at_100
+      value: 12.613
+    - type: recall_at_1000
+      value: 36.937
+    - type: recall_at_3
+      value: 1.802
+    - type: recall_at_5
+      value: 1.802
+  - task:
+      type: BitextMining
+    dataset:
+      type: rbawden/DiaBLa
+      name: MTEB DiaBLaBitextMining (fr-en)
+      config: fr-en
+      split: test
+      revision: 5345895c56a601afe1a98519ce3199be60a27dba
+    metrics:
+    - type: accuracy
+      value: 88.90048712595686
+    - type: f1
+      value: 86.94952864886115
+    - type: precision
+      value: 86.20344379175826
+    - type: recall
+      value: 88.90048712595686
+  - task:
+      type: Clustering
+    dataset:
+      type: lyon-nlp/clustering-hal-s2s
+      name: MTEB HALClusteringS2S
+      config: default
+      split: test
+      revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915
+    metrics:
+    - type: v_measure
+      value: 24.087988843991155
+  - task:
+      type: Clustering
+    dataset:
+      type: mlsum
+      name: MTEB MLSUMClusteringP2P
+      config: default
+      split: test
+      revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
+    metrics:
+    - type: v_measure
+      value: 43.79603865728535
+  - task:
+      type: Clustering
+    dataset:
+      type: mlsum
+      name: MTEB MLSUMClusteringS2S
+      config: default
+      split: test
+      revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
+    metrics:
+    - type: v_measure
+      value: 37.746550373003
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (fr)
+      config: fr
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
+    - type: accuracy
+      value: 89.26088318196052
+    - type: f1
+      value: 88.95811185929033
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (fr)
+      config: fr
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 68.55308487316003
+    - type: f1
+      value: 48.2936682439785
+  - task:
+      type: Classification
+    dataset:
+      type: masakhane/masakhanews
+      name: MTEB MasakhaNEWSClassification (fra)
+      config: fra
+      split: test
+      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
+    metrics:
+    - type: accuracy
+      value: 81.51658767772511
+    - type: f1
+      value: 77.695234448912
+  - task:
+      type: Clustering
+    dataset:
+      type: masakhane/masakhanews
+      name: MTEB MasakhaNEWSClusteringP2P (fra)
+      config: fra
+      split: test
+      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
+    metrics:
+    - type: v_measure
+      value: 40.80377094681114
+  - task:
+      type: Clustering
+    dataset:
+      type: masakhane/masakhanews
+      name: MTEB MasakhaNEWSClusteringS2S (fra)
+      config: fra
+      split: test
+      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
+    metrics:
+    - type: v_measure
+      value: 28.79703837416241
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (fr)
+      config: fr
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 67.40080699394755
+    - type: f1
+      value: 65.60793135686376
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (fr)
+      config: fr
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 71.29455279085406
+    - type: f1
+      value: 70.80876673828983
+  - task:
+      type: Retrieval
+    dataset:
+      type: jinaai/mintakaqa
+      name: MTEB MintakaRetrieval (fr)
+      config: fr
+      split: test
+      revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
+    metrics:
+    - type: map_at_1
+      value: 16.625999999999998
+    - type: map_at_10
+      value: 25.224999999999998
+    - type: map_at_100
+      value: 26.291999999999998
+    - type: map_at_1000
+      value: 26.395000000000003
+    - type: map_at_3
+      value: 22.378999999999998
+    - type: map_at_5
+      value: 24.009
+    - type: mrr_at_1
+      value: 16.625999999999998
+    - type: mrr_at_10
+      value: 25.224999999999998
+    - type: mrr_at_100
+      value: 26.291999999999998
+    - type: mrr_at_1000
+      value: 26.395000000000003
+    - type: mrr_at_3
+      value: 22.378999999999998
+    - type: mrr_at_5
+      value: 24.009
+    - type: ndcg_at_1
+      value: 16.625999999999998
+    - type: ndcg_at_10
+      value: 30.074
+    - type: ndcg_at_100
+      value: 35.683
+    - type: ndcg_at_1000
+      value: 38.714999999999996
+    - type: ndcg_at_3
+      value: 24.188000000000002
+    - type: ndcg_at_5
+      value: 27.124
+    - type: precision_at_1
+      value: 16.625999999999998
+    - type: precision_at_10
+      value: 4.566
+    - type: precision_at_100
+      value: 0.729
+    - type: precision_at_1000
+      value: 0.097
+    - type: precision_at_3
+      value: 9.801
+    - type: precision_at_5
+      value: 7.305000000000001
+    - type: recall_at_1
+      value: 16.625999999999998
+    - type: recall_at_10
+      value: 45.659
+    - type: recall_at_100
+      value: 72.85000000000001
+    - type: recall_at_1000
+      value: 97.42
+    - type: recall_at_3
+      value: 29.402
+    - type: recall_at_5
+      value: 36.527
+  - task:
+      type: PairClassification
+    dataset:
+      type: GEM/opusparcus
+      name: MTEB OpusparcusPC (fr)
+      config: fr
+      split: test
+      revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
+    metrics:
+    - type: cos_sim_accuracy
+      value: 83.58310626702998
+    - type: cos_sim_ap
+      value: 94.01979957812989
+    - type: cos_sim_f1
+      value: 88.70135958743555
+    - type: cos_sim_precision
+      value: 84.01420959147424
+    - type: cos_sim_recall
+      value: 93.94240317775571
+    - type: dot_accuracy
+      value: 83.58310626702998
+    - type: dot_ap
+      value: 94.01979957812989
+    - type: dot_f1
+      value: 88.70135958743555
+    - type: dot_precision
+      value: 84.01420959147424
+    - type: dot_recall
+      value: 93.94240317775571
+    - type: euclidean_accuracy
+      value: 83.58310626702998
+    - type: euclidean_ap
+      value: 94.01979957812989
+    - type: euclidean_f1
+      value: 88.70135958743555
+    - type: euclidean_precision
+      value: 84.01420959147424
+    - type: euclidean_recall
+      value: 93.94240317775571
+    - type: manhattan_accuracy
+      value: 83.58310626702998
+    - type: manhattan_ap
+      value: 93.99936024003892
+    - type: manhattan_f1
+      value: 88.6924150767799
+    - type: manhattan_precision
+      value: 83.45008756567425
+    - type: manhattan_recall
+      value: 94.63753723932473
+    - type: max_accuracy
+      value: 83.58310626702998
+    - type: max_ap
+      value: 94.01979957812989
+    - type: max_f1
+      value: 88.70135958743555
+  - task:
+      type: PairClassification
+    dataset:
+      type: paws-x
+      name: MTEB PawsX (fr)
+      config: fr
+      split: test
+      revision: 8a04d940a42cd40658986fdd8e3da561533a3646
+    metrics:
+    - type: cos_sim_accuracy
+      value: 60.6
+    - type: cos_sim_ap
+      value: 60.18915797975459
+    - type: cos_sim_f1
+      value: 62.491349480968864
+    - type: cos_sim_precision
+      value: 45.44539506794162
+    - type: cos_sim_recall
+      value: 100
+    - type: dot_accuracy
+      value: 60.6
+    - type: dot_ap
+      value: 60.091135216056024
+    - type: dot_f1
+      value: 62.491349480968864
+    - type: dot_precision
+      value: 45.44539506794162
+    - type: dot_recall
+      value: 100
+    - type: euclidean_accuracy
+      value: 60.6
+    - type: euclidean_ap
+      value: 60.18915797975459
+    - type: euclidean_f1
+      value: 62.491349480968864
+    - type: euclidean_precision
+      value: 45.44539506794162
+    - type: euclidean_recall
+      value: 100
+    - type: manhattan_accuracy
+      value: 60.650000000000006
+    - type: manhattan_ap
+      value: 60.2082343915352
+    - type: manhattan_f1
+      value: 62.491349480968864
+    - type: manhattan_precision
+      value: 45.44539506794162
+    - type: manhattan_recall
+      value: 100
+    - type: max_accuracy
+      value: 60.650000000000006
+    - type: max_ap
+      value: 60.2082343915352
+    - type: max_f1
+      value: 62.491349480968864
+  - task:
+      type: STS
+    dataset:
+      type: Lajavaness/SICK-fr
+      name: MTEB SICKFr
+      config: default
+      split: test
+      revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
+    metrics:
+    - type: cos_sim_pearson
+      value: 79.77067200230256
+    - type: cos_sim_spearman
+      value: 76.7445532523278
+    - type: euclidean_pearson
+      value: 76.34017074673956
+    - type: euclidean_spearman
+      value: 76.7453011027832
+    - type: manhattan_pearson
+      value: 76.19578084197778
+    - type: manhattan_spearman
+      value: 76.56293456459228
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts22-crosslingual-sts
+      name: MTEB STS22 (fr)
+      config: fr
+      split: test
+      revision: eea2b4fe26a775864c896887d910b76a8098ad3f
+    metrics:
+    - type: cos_sim_pearson
+      value: 81.2564160237984
+    - type: cos_sim_spearman
+      value: 83.30552085410882
+    - type: euclidean_pearson
+      value: 82.00494560507786
+    - type: euclidean_spearman
+      value: 83.30552085410882
+    - type: manhattan_pearson
+      value: 81.93132229157803
+    - type: manhattan_spearman
+      value: 83.04357992939353
+  - task:
+      type: STS
+    dataset:
+      type: stsb_multi_mt
+      name: MTEB STSBenchmarkMultilingualSTS (fr)
+      config: fr
+      split: test
+      revision: 93d57ef91790589e3ce9c365164337a8a78b7632
+    metrics:
+    - type: cos_sim_pearson
+      value: 80.34931905288978
+    - type: cos_sim_spearman
+      value: 79.99372771100049
+    - type: euclidean_pearson
+      value: 78.37976845123443
+    - type: euclidean_spearman
+      value: 79.99452356550658
+    - type: manhattan_pearson
+      value: 78.24434042082316
+    - type: manhattan_spearman
+      value: 79.87248340061164
+  - task:
+      type: Summarization
+    dataset:
+      type: lyon-nlp/summarization-summeval-fr-p2p
+      name: MTEB SummEvalFr
+      config: default
+      split: test
+      revision: b385812de6a9577b6f4d0f88c6a6e35395a94054
+    metrics:
+    - type: cos_sim_pearson
+      value: 30.476001473421586
+    - type: cos_sim_spearman
+      value: 29.687350195905456
+    - type: dot_pearson
+      value: 30.476000875190685
+    - type: dot_spearman
+      value: 29.662224660056562
+  - task:
+      type: Reranking
+    dataset:
+      type: lyon-nlp/mteb-fr-reranking-syntec-s2p
+      name: MTEB SyntecReranking
+      config: default
+      split: test
+      revision: b205c5084a0934ce8af14338bf03feb19499c84d
+    metrics:
+    - type: map
+      value: 88.28333333333333
+    - type: mrr
+      value: 88.28333333333333
+  - task:
+      type: Retrieval
+    dataset:
+      type: lyon-nlp/mteb-fr-retrieval-syntec-s2p
+      name: MTEB SyntecRetrieval
+      config: default
+      split: test
+      revision: 77f7e271bf4a92b24fce5119f3486b583ca016ff
+    metrics:
+    - type: map_at_1
+      value: 69
+    - type: map_at_10
+      value: 79.906
+    - type: map_at_100
+      value: 79.982
+    - type: map_at_1000
+      value: 79.982
+    - type: map_at_3
+      value: 77.667
+    - type: map_at_5
+      value: 79.51700000000001
+    - type: mrr_at_1
+      value: 69
+    - type: mrr_at_10
+      value: 79.906
+    - type: mrr_at_100
+      value: 79.982
+    - type: mrr_at_1000
+      value: 79.982
+    - type: mrr_at_3
+      value: 77.667
+    - type: mrr_at_5
+      value: 79.51700000000001
+    - type: ndcg_at_1
+      value: 69
+    - type: ndcg_at_10
+      value: 84.60499999999999
+    - type: ndcg_at_100
+      value: 84.868
+    - type: ndcg_at_1000
+      value: 84.868
+    - type: ndcg_at_3
+      value: 80.333
+    - type: ndcg_at_5
+      value: 83.647
+    - type: precision_at_1
+      value: 69
+    - type: precision_at_10
+      value: 9.9
+    - type: precision_at_100
+      value: 1
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_3
+      value: 29.333
+    - type: precision_at_5
+      value: 19.2
+    - type: recall_at_1
+      value: 69
+    - type: recall_at_10
+      value: 99
+    - type: recall_at_100
+      value: 100
+    - type: recall_at_1000
+      value: 100
+    - type: recall_at_3
+      value: 88
+    - type: recall_at_5
+      value: 96
+  - task:
+      type: Retrieval
+    dataset:
+      type: jinaai/xpqa
+      name: MTEB XPQARetrieval (fr)
+      config: fr
+      split: test
+      revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f
+    metrics:
+    - type: map_at_1
+      value: 42.027
+    - type: map_at_10
+      value: 64.331
+    - type: map_at_100
+      value: 65.657
+    - type: map_at_1000
+      value: 65.7
+    - type: map_at_3
+      value: 57.967999999999996
+    - type: map_at_5
+      value: 62.33800000000001
+    - type: mrr_at_1
+      value: 65.688
+    - type: mrr_at_10
+      value: 72.263
+    - type: mrr_at_100
+      value: 72.679
+    - type: mrr_at_1000
+      value: 72.69099999999999
+    - type: mrr_at_3
+      value: 70.405
+    - type: mrr_at_5
+      value: 71.587
+    - type: ndcg_at_1
+      value: 65.688
+    - type: ndcg_at_10
+      value: 70.221
+    - type: ndcg_at_100
+      value: 74.457
+    - type: ndcg_at_1000
+      value: 75.178
+    - type: ndcg_at_3
+      value: 65.423
+    - type: ndcg_at_5
+      value: 67.05499999999999
+    - type: precision_at_1
+      value: 65.688
+    - type: precision_at_10
+      value: 16.208
+    - type: precision_at_100
+      value: 1.975
+    - type: precision_at_1000
+      value: 0.207
+    - type: precision_at_3
+      value: 39.831
+    - type: precision_at_5
+      value: 28.652
+    - type: recall_at_1
+      value: 42.027
+    - type: recall_at_10
+      value: 78.803
+    - type: recall_at_100
+      value: 95.051
+    - type: recall_at_1000
+      value: 99.75500000000001
+    - type: recall_at_3
+      value: 62.62799999999999
+    - type: recall_at_5
+      value: 70.975
+license: mit
+language:
+- fr
+---
+
+# Solon Embeddings — large 0.1
+
+SOTA Open source french embedding model.
+
+**Instructions :**  
+Add "query : " before the *query* to retrieve to increase performance of retrieval.  
+No instructions needed for *passages*.
+
+
+| Model | Mean Score |
+| --- | --- |
+| **OrdalieTech/Solon-embeddings-large-0.1** | 0.7490 |
+| cohere/embed-multilingual-v3 | 0.7402 |
+| **OrdalieTech/Solon-embeddings-base-0.1** | 0.7306 |
+| openai/ada-002 | 0.7290 |
+| cohere/embed-multilingual-light-v3 | 0.6945 |
+| antoinelouis/biencoder-camembert-base-mmarcoFR | 0.6826 |
+| dangvantuan/sentence-camembert-large | 0.6756 |
+| voyage/voyage-01 | 0.6753 |
+| intfloat/multilingual-e5-large | 0.6660 |
+| intfloat/multilingual-e5-base | 0.6597 |
+| Sbert/paraphrase-multilingual-mpnet-base-v2 | 0.5975 |
+| dangvantuan/sentence-camembert-base | 0.5456 |
+| EuropeanParliament/eubert_embedding_v1 | 0.5063 |
+
+These results have been obtained through 9 french benchmarks on a variety of text similarity tasks (classification, reranking, STS) :
+- AmazonReviewsClassification (MTEB)
+- MassiveIntentClassification (MTEB)
+- MassiveScenarioClassification (MTEB)
+- MTOPDomainClassification (MTEB)
+- MTOPIntentClassification (MTEB)
+- STS22 (MTEB)
+- MiraclFRRerank (Miracl)
+- OrdalieFRSTS (Ordalie)
+- OrdalieFRReranking (Ordalie)
+
+We created OrdalieFRSTS and OrdalieFRReranking to enhance the benchmarking capabilities of French STS and reranking assessments.
+
+(evaluation script available here : github.com/OrdalieTech/mteb)

config.json

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+{
+  "architectures": [
+    "XLMRobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
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+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.55.4",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "5.1.2",
+    "transformers": "4.55.4",
+    "pytorch": "2.9.0+cpu"
+  },
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

modules.json

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+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
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+    "type": "sentence_transformers.models.Pooling"
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+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

onnx/model.onnx

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onnx/model.onnx_data

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sentence_bert_config.json

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+    "do_lower_case": false
+}

special_tokens_map.json

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+}